Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

Ozan KOACADAGLİ

Makale detayları

Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

Yazarlar:

Ozan KOACADAGLİ

Yayıncı:

Giresun University

Ana Başlıklar:

Matematik

Dergi:

Turkish Journal of Forecasting 2017 Vol.01 Issue 2, pp.54-65

Belge Tipi:

Makale

Yayın Tarihi:

12/2017

Belge Dili:

İngilizce

Document ID (Org No.):

2000637139

Sıklık:

İki Yıllık

eISSN:

2618-6594

Özet:

 


In the nonlinear
systems, the pre-knowledge about the exact functional structure between inputs
and outputs is mostly either unavailable or insufficient. In this case, the
artificial neural networks (ANNs) are useful tools to estimate this functional
structure. However, the
traditional ANNs with the sum squared error suffer from the approximation and
estimation errors in the high dimensional and excessive nonlinear cases. In
this context, Bayesian neural networks (BNNs) provide a natural way to
alleviate these issues by means of penalizing the excessive complex models.
Thus, this approach allows estimating more reliable and robust models in the
regression analysis, time series, pattern recognition problems etc. This paper
presents a Bayesian learning approach based on Gaussian approximation which
estimates the parameters and hyperparameters in the BNNs efficiently. In the
application part, the proposed approach is compared with the traditional ANNs
in terms of their estimation and prediction performances over an artificial
data set.


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Makale detayları

Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

Yazarlar: Ozan KOACADAGLİ

Yayıncı: Giresun University

Ana Başlıklar: Matematik

Dergi: Turkish Journal of Forecasting 2017 Vol.01 Issue 2, pp.54-65

Belge Tipi: Makale

Yayın Tarihi: 12/2017

Belge Dili: İngilizce

Document ID (Org No.): 2000637139

Sıklık: İki Yıllık

eISSN: 2618-6594

Sayı

Yazarlar:

Ozan KOACADAGLİ

Yayıncı:

Giresun University

Ana Başlıklar:

Matematik

Dergi:

Turkish Journal of Forecasting 2017 Vol.01 Issue 2, pp.54-65

Belge Tipi:

Makale

Yayın Tarihi:

12/2017

Belge Dili:

İngilizce

Document ID (Org No.):

2000637139

Sıklık:

İki Yıllık

eISSN:

2618-6594